Method and apparatus for contextual content suggestion

ABSTRACT

An approach is provided for contextual content suggestion. A recommendation platform processes and/or facilitates a processing of contextual information associated with at least one device to determine one or more locations, one or more contextual parameter values, or a combination thereof. The recommendation platform also determines popularity data associated with one or more content items with respect to the one or more locations, the one or more contextual parameter values, or a combination. The popularity data is determined from one or more other devices sharing at least substantially the one or more locations, the one or more contextual parameter values, or a combination thereof. The recommendation platform then causes, at least in part, a recommendation of the one or more content items to the at least one device based, at least in part, on the popularity information.

BACKGROUND

Wireless (e.g., cellular) service providers and device manufacturers arecontinually challenged to deliver value and convenience to consumers by,for example, providing compelling network services, applications, andcontent. One area of development relates to providing services to assistusers in filtering through the growing amounts and varieties ofavailable services, applications, and content to discover content ofinterest. However, such services often rely on potentially burdensomemanual user configuration and/or effort, which can lead to a poor userexperience and lower usage rates. Accordingly, service providers anddevice manufacturers face significant technical challenges to overcomingsuch burdens by enabling efficient content suggestion and discovery.

SOME EXEMPLARY EMBODIMENTS

Therefore, there is a need for an approach for providingcontextually-based content suggestion or recommendation that minimizesthe burden placed on users.

According to one embodiment, a method comprises processing and/orfacilitating a processing of contextual information associated with atleast one device to determine one or more locations, one or morecontextual parameter values, or a combination thereof. The methodfurther comprises determining popularity data associated with one ormore content items with respect to the one or more locations, the one ormore contextual parameter values, or a combination. The popularity datais determined from one or more other devices sharing at leastsubstantially the one or more locations, the one or more contextualparameter values, or a combination thereof. The method also comprisescausing, at least in part, a recommendation of the one or more contentitems to the at least one device based, at least in part, on thepopularity information.

According to another embodiment, an apparatus comprises at least oneprocessor, and at least one memory including computer program code forone or more programs, the at least one memory and the computer programcode configured to, with the at least one processor, cause, at least inpart, the apparatus to process and/or facilitate a processing ofcontextual information associated with at least one device to determineone or more locations, one or more contextual parameter values, or acombination thereof. The apparatus is further caused to determinepopularity data associated with one or more content items with respectto the one or more locations, the one or more contextual parametervalues, or a combination. The popularity data is determined from one ormore other devices sharing at least substantially the one or morelocations, the one or more contextual parameter values, or a combinationthereof. The apparatus also causes, at least in part, a recommendationof the one or more content items to the at least one device based, atleast in part, on the popularity information.

According to another embodiment, a computer-readable storage mediumcarries one or more sequences of one or more instructions which, whenexecuted by one or more processors, cause, at least in part, anapparatus to process and/or facilitate a processing of contextualinformation associated with at least one device to determine one or morelocations, one or more contextual parameter values, or a combinationthereof. The apparatus is further caused to determine popularity dataassociated with one or more content items with respect to the one ormore locations, the one or more contextual parameter values, or acombination. The popularity data is determined from one or more otherdevices sharing at least substantially the one or more locations, theone or more contextual parameter values, or a combination thereof. Theapparatus also causes, at least in part, a recommendation of the one ormore content items to the at least one device based, at least in part,on the popularity information.

According to another embodiment, an apparatus comprises means forprocessing and/or facilitating a processing of contextual informationassociated with at least one device to determine one or more locations,one or more contextual parameter values, or a combination thereof. Theapparatus further comprises means for determining popularity dataassociated with one or more content items with respect to the one ormore locations, the one or more contextual parameter values, or acombination. The popularity data is determined from one or more otherdevices sharing at least substantially the one or more locations, theone or more contextual parameter values, or a combination thereof. Theapparatus also comprises means for causing, at least in part, arecommendation of the one or more content items to the at least onedevice based, at least in part, on the popularity information.

In addition, for various example embodiments of the invention, thefollowing is applicable: a method comprising facilitating a processingof and/or processing (1) data and/or (2) information and/or (3) at leastone signal, the (1) data and/or (2) information and/or (3) at least onesignal based, at least in part, on (including derived at least in partfrom) any one or any combination of methods (or processes) disclosed inthis application as relevant to any embodiment of the invention.

For various example embodiments of the invention, the following is alsoapplicable: a method comprising facilitating access to at least oneinterface configured to allow access to at least one service, the atleast one service configured to perform any one or any combination ofnetwork or service provider methods (or processes) disclosed in thisapplication.

For various example embodiments of the invention, the following is alsoapplicable: a method comprising facilitating creating and/orfacilitating modifying (1) at least one device user interface elementand/or (2) at least one device user interface functionality, the (1) atleast one device user interface element and/or (2) at least one deviceuser interface functionality based, at least in part, on data and/orinformation resulting from one or any combination of methods orprocesses disclosed in this application as relevant to any embodiment ofthe invention, and/or at least one signal resulting from one or anycombination of methods (or processes) disclosed in this application asrelevant to any embodiment of the invention.

For various example embodiments of the invention, the following is alsoapplicable: a method comprising creating and/or modifying (1) at leastone device user interface element and/or (2) at least one device userinterface functionality, the (1) at least one device user interfaceelement and/or (2) at least one device user interface functionalitybased at least in part on data and/or information resulting from one orany combination of methods (or processes) disclosed in this applicationas relevant to any embodiment of the invention, and/or at least onesignal resulting from one or any combination of methods (or processes)disclosed in this application as relevant to any embodiment of theinvention.

In various example embodiments, the methods (or processes) can beaccomplished on the service provider side or on the mobile device sideor in any shared way between service provider and mobile device withactions being performed on both sides.

For various example embodiments, the following is applicable: Anapparatus comprising means for performing the method of any oforiginally filed claims 1-10, 21-30, and 46-48.

Still other aspects, features, and advantages of the invention arereadily apparent from the following detailed description, simply byillustrating a number of particular embodiments and implementations,including the best mode contemplated for carrying out the invention. Theinvention is also capable of other and different embodiments, and itsseveral details can be modified in various obvious respects, all withoutdeparting from the spirit and scope of the invention. Accordingly, thedrawings and description are to be regarded as illustrative in nature,and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the invention are illustrated by way of example, andnot by way of limitation, in the figures of the accompanying drawings:

FIG. 1A is a diagram of a system capable of providing contextual contentsuggestions, according to one embodiment;

FIG. 1B is a diagram of a geographic database used for providingcontextual content suggestions, according to one embodiment;

FIG. 2 is a diagram of components of a recommendation platform forproviding contextual content suggestions, according to one embodiment;

FIG. 3 is a flowchart of a process for providing contextual contentsuggestions, according to one embodiment;

FIG. 4 is a flowchart of a process for collecting content accesshistories to facilitate providing contextual content suggestions,according to one embodiment;

FIG. 5 is a flowchart of a process for top level domain filtering ofcontent access histories, according to one embodiment;

FIG. 6 is a flowchart of a process for decaying popularity data used fordetermining contextual content suggestions, according to one embodiment;

FIG. 7 is a flowchart of a process for managing popularity data decay byspecifying maximum popularity values, according to one embodiment;

FIG. 8 is a diagram depicting a decay of popularity data via geometricprogression, according to one embodiment;

FIGS. 9A-9D are diagrams of depicting an overview of providingcontextual content recommendations, according to various embodiments;

FIG. 10 is a diagram of a user interface reflecting the processes ofFIGS. 1-9, according to one embodiment;

FIG. 11 is a diagram of hardware that can be used to implement anembodiment of the invention;

FIG. 12 is a diagram of a chip set that can be used to implement anembodiment of the invention; and

FIG. 13 is a diagram of a mobile station (e.g., handset) that can beused to implement an embodiment of the invention.

DESCRIPTION OF PREFERRED EMBODIMENT

A method and apparatus for providing contextual content suggestion aredisclosed. In the following description, for the purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of the embodiments of the invention. It isapparent, however, to one skilled in the art that the embodiments of theinvention may be practiced without these specific details or with anequivalent arrangement. In other instances, well-known structures anddevices are shown in block diagram form in order to avoid unnecessarilyobscuring the embodiments of the invention.

FIG. 1 is a diagram of a system capable of providing contextual contentsuggestions, according to one embodiment. As noted above, contentdiscovery can be problematic for users. For example, with respect toweb-based content, in order for web browser users to discover content onthe web, the users generally have to either manually enter an addressfor the content (e.g., a Universal Resource Identifier (URI) such as aUniversal Resource Locator (URL)) or click on a provided link. Thismeans, for instance, that the user has to know the URI of the content orthat the provided link is of value to the specific user. This limitationcan make discovery of new and/or relevant content difficult.

Historically, there are many services that try to assist users indiscovering content. However, most have an incomplete view of the userscontent (e.g., location and/or other context parameters such as time,activity, etc.) and, therefore, generally cannot provide relevantcontent suggestions or recommendations without requiring extra effort bythe user (e.g., checking-in, joining a social graph, performingsearches, etc.).

To address this problem, a system 100 of FIG. 1 introduces thecapability to provide contextual content suggestions by leveraging theaggregate usage patterns of a crowd (e.g., a group of devices). In oneembodiment, the aggregate usage patterns are determined in real time orsubstantially real time to calculate the popularity (also referred to aspopularity data or “traction”) of various content items as a functionone or more user contexts (e.g., location, time, activity, role, intent,etc.). The system 100 can then use the popularity data or traction tomake content suggestions based on a specific user's context.

In other words, the system 100 records content access or usage patterns(e.g., web browsing patterns) of users as relative their specificcontexts. When a particular user accesses the system 100 under aspecific context (e.g., at a specific location at a certain time), thesystem 100 will present that particular user with a list of contentsuggestions (e.g., web links) that are relevant to the user based on thepopularity of the content items with other users sharing the same orsimilar contexts. The various embodiments of the approach describedherein differ from other recommendation engines in that the system 100does not require any active participation by its users. For example, thevarious embodiments are a passive solution where users do not need tocreate an account, check-in, perform searches, etc. in order to discovercontent.

One potential use case for the various embodiments for providingcontextual content suggestions described herein is targetedadvertisement. Targeted advertisement is very important to advertisers(e.g., business owners) since this kind of advertisement typicallyyields better results (e.g., click through rates) than non-targetedadvertisements. However, even if some web sites have the users profiledenough to render targeted advertisements that are relevant regardless ofthe users' contexts (e.g., where their users are geographically), atsome point these advertisements can become irrelevant. For example, auser who likes skiing might get advertisements about skiing equipment,but if the user is temporarily in another context such as a sunny beachlocation, advertisement about skiing equipment will most likely beirrelevant. Instead, to increase the potential effectiveness of anadvertisement, the advertisement needs to reflect the context of theuser. With the approach described herein, the user need not take anyactive steps to express his or her preferences under specific contextsbecause the system 100 can make recommendations based on the popularityof content (e.g., advertising content) with other users sharing the sameor similar context.

In one embodiment, the content items may be suggested to the system 100using crowdsourcing means. In addition or alternatively, businessowners, government agencies, and/or any person/entity may activelypromote or suggest content items for recommendation by the system 100.In one embodiment, the person or entity who wishes to promote a contentitem (e.g., a link to their web site) can make use of the system 100 tomake sure that their content item is seen by people accessing the system100 under defined contexts. For example, a restaurant owner can use thesystem 100 to introduce a link or content item in a relevant geographicarea. However, in order for the link or content item to become popularand therefore seen by users in the area, the link or content item has tobe popular. For this case, there are, for instance, two possiblescenarios: (1) the link or content item becomes popular on its ownmerits, which will imply that users are requesting the link or contentitem independently; or (2) the business owner can pay to add traction orpopularity to the link and force the link to be popular and therefore beseen by users.

In one embodiment, the system 100 implements a popular decay mechanismwhereby if a user clicks on said link, the link can stay popular orincrease in traction in the system 100. However, if people do not clickon the link or content item, the link will eventually decrease inpopularity (e.g., decay) to a point where it is removed from the systemdue to lack of popularity or traction.

As shown in FIG. 1, the system 100 comprises a recommendation platform101 operating in conjunction with a proxy server 103 that provides forproxy web browsing over the communication network 105. Therecommendation platform 101 processes content usage or access patternsto determine popularity data or traction for content items underspecific contexts (e.g., location, time, and other contextualparameters). In one embodiment, the content usage or access patterns arestored in the access history database 107. In another embodiment, thesystem 100 enables users (e.g., via user equipment (UEs) 109 a-109n—also collectively referred to as UEs 109) to be able to receivecontextual content suggestions by way of the recommendation platform101.

In one embodiment, the proxy server 103 is part of a proxy browsingarchitecture that centralizes the collection of content usage or accesspatterns. However, it is noted that although the various embodiments aredescribed with respect to determining context usage patterns via theproxy server 103, it is contemplated that any other architecture (e.g.,architectures that are not based on proxy browsing) that facilitatescollection of content usage or access patterns may be used. For example,the UEs 109 may directly report (e.g., periodically or continuously)content usage or access patterns to the recommendation platform 101. Inaddition, although the recommendation platform 101 and the proxy server103 are depicted as separate components, in some embodiments, thefunctions of the recommendation platform 101 may be included as orperformed by one or more components of the proxy server 103 or any othercomponent of the system 100. Similarly, in other embodiments, thefunctions of the proxy server 103 may be included in or performed by oneor more components of the recommendation platform 101 or any othercomponents of the system 100.

By way of example, proxy browsing is a technology that reduces theamount of data that needs to be transferred between a web server and aweb browser. An intermediate proxy server located between a mobiledevice and the Internet may, for example, be used to reduce image sizes,simplify the HTML markup of a webpage, and compress transmitted data.Proxy browsing also allows for a reduction in hardware requirements forinternet enabled mobile devices, faster rendering of webpages, andreduced bandwidth usage.

In addition to the proxy server 103, the proxy browsing architecturedepicted in the example of FIG. 1 includes of one or more proxy clients111 a-111 n (also collectively referred to as clients 111) operatingwithin respective client devices (e.g., UEs 109 a-109 n). In oneembodiment, the proxy clients 111 route at least a portion of thecommunication traffic from the UEs 109 through the proxy server 103. Insome embodiments, the proxy clients 111 may be a browser application ora simplified version of a browser application. In addition oralternatively, the proxy clients 111 can be independent processesexecuting in the UEs 109, or can be incorporated in other applicationsexecuting in the UEs 109.

In one embodiment, the recommendation platform 101 detects when UEs 109access the platform 101 and/or any other component of the system 100 andthen provides contextually relevant content suggestions to the UEs 109based on other devices sharing the same or similar contexts. In oneembodiment, the proxy server 103 detects the access by the UEs 109 by,for instance, receiving requests from the proxy clients 111 to routecommunication traffic to the intended communication endpoints providingassociated content items or links. In addition, the proxy server 103 canroute return communication traffic from the communication endpoints tothe any of the proxy clients 111 and/or UEs 109. By way of example, thecommunication endpoints can include a service platform 113, services 115a-115 m (also collectively referred to as services 115), contentproviders 117 a-117 k (also collectively referred to as contentproviders 117), or any other component with connectivity to thecommunication network 105 (e.g., another UE 109). For example, theservice platform 113, the service 115, and/or the content providers 117may provide any number of content items, links, services, etc. (e.g.,mapping services, social networking services, media services, contentservices, etc.) via a web server or other means of communications (e.g.,text messaging, voice, instant messaging, chat, etc.). In other words,the communication endpoints represent a terminating point ofcommunications from the proxy clients 111, and an originating point ofcommunications to the proxy clients 111.

In some embodiments, the proxy server 103 receives requests from theproxy clients 111 to access service content, such as a webpage, webapplication, or other web content, and the proxy server 103 can performany number of communications related functions for routing and/orprocessing the resulting communication traffic. For example, as notedabove, the proxy server 103 can provide an optimized distributed scriptprocessing experience by delivering only new images of the web content,providing partial updates based on document mutations or differences,enabling pass-through or scripts for on-device changes (e.g., changes toCSS properties, CSS3 transitions, etc.). In other embodiments, the proxyserver 103 may compress or otherwise modify content that is to bedelivered to the proxy clients 111 based, at least in part, on one ormore capabilities or characteristics of the receiving UE 109. Forexample, in wireless environments, the proxy server 103 can compressdata for more efficient transmission, transform content to reduce theamount of data for transfer, reformat content for display in smallerscreens, change the content to an image file, etc. The proxy server 103may divide the service content into a series of subparts that may beequally or unequally parsed and sent to the UE 109 like a deck of cardsbased on any of the display capabilities or resolution of a display,available memory, a battery condition, and/or available power modesettings of the UE 109. In one embodiment, the proxy server 103 recordsthe content requests, communication traffic, and/or related data ascontent usage or access patterns for storage in the access historydatabase 107.

By way of example, the UE 109 is any type of mobile terminal, fixedterminal, or portable terminal including a mobile handset, station,unit, device, multimedia computer, multimedia tablet, Internet node,communicator, desktop computer, laptop computer, notebook computer,netbook computer, tablet computer, personal communication system (PCS)device, personal navigation device, personal digital assistants (PDAs),audio/video player, digital camera/camcorder, positioning device,television receiver, radio broadcast receiver, electronic book device,game device, or any combination thereof, including the accessories andperipherals of these devices, or any combination thereof. It is alsocontemplated that the UE 109 can support any type of interface to theuser (such as “wearable” circuitry, etc.).

In one embodiment, the UE 101 may also include or be associated with oneor more sensors 119 a-119 n (collectively referred to as sensors 119)for determining location information and/or other contextual parameters.The sensors 119 may include any type of sensor that is compatible withthe UE 109, such as a light sensor, a pressure sensor (e.g., barometer),a motion sensor (e.g., accelerometer), a location sensor (e.g., GPS), adirection sensor, an image sensor (e.g., camera), a sound sensor (e.g.,microphone), etc. For example, a location sensor in addition to adirection sensor may allow the UE 109 to determine the location of theUE 109 as well as a direction that the UE 109 faces to assist indetermining one or more contexts associated with the UE 109.

In another embodiment, the outputs determined from the sensors 119 maybe correlated to one or more contextual databases to determineparticular contextual values. For example, the geocoordinates obtainedfrom a location sensor may be evaluated against a geographic database121 to correlate the geocoordinates to specific geographic features,areas, locations, etc. (e.g., road networks, paths, points of interest,etc. for different modes of transport including walking, driving,biking, etc.).

FIG. 1B depicts an example geographic database 121 that containsgeographic data 135 that represents some of the physical geographicfeatures in one or more geographic areas associated with the UE 109. Byway of example, the data 135 contained in the geographic database 121includes data that represent one or more road networks. Accordingly, inone embodiment of FIG. 1B, the geographic database 121 contains at leastone road segment data record 123 for each road segment in the geographicregion of interest. The geographic database 121 also includes a nodedata record 125 for each node in the geographic region. The terms“nodes” and “segments” represent only one terminology for describingthese physical geographic features, and other terminology for describingthese features is intended to be encompassed within the scope of theseconcepts. For example, the geographic database 121 may also include datafor specific modes of transport such as pedestrian segment data records127 and pedestrian orientation node records 129.

In one embodiment, the geographic database 121 may also include otherkinds of data 131. The other kinds of data 131 may represent other kindsof geographic features or anything else such as points of interest data.For example, the point of interest data may include point of interestinformation comprising a type (e.g., the type of point of interest, suchas restaurant, hotel, city hall, police station, historical marker,banking center, golf course, etc.), location of the point of interest, aphone number, hours of operation, etc. The geographic database 121 alsomay include indexes 133. In one embodiment, the indexes 133 may includevarious types of indexes that relate to other aspects of the datacontained in the geographic database 121. For example, the indexes 133may relate to the nodes in the node data records 125 with the end pointsof a road segment in the road segment data records 123. As anotherexample, the indexes 133 may relate point of interest data in the otherdata records 131 with a road segment in the segment data records 127.

Additionally, the communication network 105 of system 100 includes oneor more networks such as a data network (not shown), a wireless network(not shown), a telephony network (not shown), or any combinationthereof. It is contemplated that the data network may be any local areanetwork (LAN), metropolitan area network (MAN), wide area network (WAN),a public data network (e.g., the Internet), short range wirelessnetwork, or any other suitable packet-switched network, such as acommercially owned, proprietary packet-switched network, e.g., aproprietary cable or fiber-optic network, and the like, or anycombination thereof. In addition, the wireless network may be, forexample, a cellular network and may employ various technologiesincluding enhanced data rates for global evolution (EDGE), generalpacket radio service (GPRS), global system for mobile communications(GSM), Internet protocol multimedia subsystem (IMS), universal mobiletelecommunications system (UMTS), etc., as well as any other suitablewireless medium, e.g., worldwide interoperability for microwave access(WiMAX), Long Term Evolution (LTE) networks, code division multipleaccess (CDMA), wideband code division multiple access (WCDMA), wirelessfidelity (WiFi), wireless LAN (WLAN), Bluetooth®, Internet Protocol (IP)data casting, satellite, mobile ad-hoc network (MANET), and the like, orany combination thereof.

Communication is facilitated among the recommendation platform 101, theproxy server 103, the UEs 109, and the clients 111 via the communicationnetwork 105 using well known, new or still developing protocols. In thiscontext, a protocol includes a set of rules defining how the networknodes within the communication network 105 interact with each otherbased on information sent over the communication links. The protocolsare effective at different layers of operation within each node, fromgenerating and receiving physical signals of various types, to selectinga link for transferring those signals, to the format of informationindicated by those signals, to identifying which software applicationexecuting on a computer system sends or receives the information. Theconceptually different layers of protocols for exchanging informationover a network are described in the Open Systems Interconnection (OSI)Reference Model.

Communications between the network nodes are typically effected byexchanging discrete packets of data. Each packet typically comprises (1)header information associated with a particular protocol, and (2)payload information that follows the header information and containsinformation that may be processed independently of that particularprotocol. In some protocols, the packet includes (3) trailer informationfollowing the payload and indicating the end of the payload information.The header includes information such as the source of the packet, itsdestination, the length of the payload, and other properties used by theprotocol. Often, the data in the payload for the particular protocolincludes a header and payload for a different protocol associated with adifferent, higher layer of the OSI Reference Model. The header for aparticular protocol typically indicates a type for the next protocolcontained in its payload. The higher layer protocol is said to beencapsulated in the lower layer protocol. The headers included in apacket traversing multiple heterogeneous networks, such as the Internet,typically include a physical (layer 1) header, a data-link (layer 2)header, an internetwork (layer 3) header and a transport (layer 4)header, and various application headers (layer 5, layer 6 and layer 7)as defined by the OSI Reference Model.

In one embodiment, the proxy clients 111, the proxy server 103, and/orthe recommendation platform 101 interact according to a client-servermodel. It is noted that the client-server model of computer processinteraction is widely known and used. According to the client-servermodel, a client process sends a message including a request to a serverprocess, and the server process responds by providing a service. Theserver process may also return a message with a response to the clientprocess. Often the client process and server process execute ondifferent computer devices, called hosts, and communicate via a networkusing one or more protocols for network communications. The term“server” is conventionally used to refer to the process that providesthe service, or the host computer on which the process operates.Similarly, the term “client” is conventionally used to refer to theprocess that makes the request, or the host computer on which theprocess operates. As used herein, the terms “client” and “server” referto the processes, rather than the host computers, unless otherwise clearfrom the context. In addition, the process performed by a server can bebroken up to run as multiple processes on multiple hosts (sometimescalled tiers) for reasons that include reliability, scalability, andredundancy, among others.

FIG. 2 is a diagram of components of a recommendation platform forproviding contextual content suggestions, according to one embodiment.By way of example, the recommendation platform 101 includes one or morecomponents for providing proxy-based sharing of access histories. It iscontemplated that the functions of these components may be combined inone or more components or performed by other components of equivalentfunctionality. In this embodiment, the recommendation platform 101includes a control logic 201, an access information collector 203, acontext determination module 205, a popularity data module 207, a decaymodule 209, an affinity determination module 211, and a recommendationengine 213.

More specifically, the control logic 201 executes at least one algorithmfor performing one or more functions of the recommendation platform 101.For example, the control logic 201 interacts with the access informationcollector 203 to record (e.g., directly or via the proxy server 103)content usage and/or access patterns. For example, when a client 111requests access to a content item (e.g., by visiting a URI of thecontent item), the system 100 can add the content item to a list contentitems that can be recommended. In one embodiment, when adding thecontent item, the access information collector 203 can tag the contentitem record with one or more contextual parameters (e.g., geolocation,time, activity, or other contextual parameters) associated with the UE109 accessing or requesting access to the content item. For example,with a respect to a geolocation contextual parameter, the accessinformation collector 203 will record the geolocation of the UE 109(e.g., the geocoordinates reflecting the position on the Earth'ssurface) associated with the UE 109 when the content (e.g., URI) wasaccessed or visited.

In addition or alternatively, the system access information collector203 may record or associate any other contextual parameter (e.g., timeof access, activity of the UE 109 or user when accessing the content,intent of the user when accessing the content, etc.) in place of or incombination with the one or more locations of the UE 109. In oneembodiment, the access information collector 203 interacts with thecontext determination module 205 to infer or otherwise determine one ormore contexts for the accessed content item by, for instance, evaluatingthe types and values of the associated contextual parameters. Forexample, accessing a content item at the same location but at differenttimes of the day may give rise to different contexts. In some cases ahierarchy of the contexts may be created with, for instance, location asa root level of the hierarchy and then branching to other levelscorresponding to different contextual parameters (e.g., time, activity,intent, etc.). Each combination of contextual parameters may give riseto different contexts that can be associated with a particular contentitem access record.

In another embodiment, in addition to associating geolocationinformation with the content item record, the access information mayfurther assign each geo-tagged content item (e.g., URI) to ageographical “box”. By way of example, boxes are used to subdividegeographical areas into smaller areas that are of a scale appropriatefor users to find nearby content. Boxes, for instance, are typicallyadministrative regions such as neighborhoods, post code regions,counties, etc. Although the term box is used, it is contemplated that abox may be of any shape (e.g., regular or irregular) or size. Anotheradvantage of using the box approach is to limit the number of accessrecords to process for generating contextual content suggestions orrecommendations. For example, the system 100 need only evaluate the boxor neighboring boxes associated with a location or predicted location ofa device to make the content suggestions or recommendations.

After adding a content item to the system 100 (e.g., based oncrowdsourcing or promotion), the access information collector 203interacts with the popularity data module 207 to determine a popularitymetric (e.g., such as the traction metric described above) or other typeof popularity data with the content item. In one embodiment, traction isa term used to describe the popularity index for a content item (e.g., aURI). In some embodiments, the traction may be determined with respectto a given geographical box or other defined geographical area. Forexample, the higher the traction, the more popular a content item orlink is said to be within its box. In one embodiment, when a contentitem or link is accessed by a client 111, the content item is given aninitial or default traction value in addition to the contextualparameter values (e.g., location, time, intent, activity, etc.)described above. As described in more detail below, additionalinteraction with the content item under the same contexts can increasethe traction or popularity (also referred to generally as popularitydata).

In order to effectively maintain a set of content items in the system100 that are relevant as a function of time, the system 100 can use amethod for reducing or decaying the popularity or traction of contentitems as the go unused overtime. Accordingly, in some embodiments, thepopularity data module 207 interacts with the decay module 209 to applyone or more decay functions to the respective traction values for thecontent items in the system. It is contemplated that any decay functionmay be used including, at least in part, time-based decay functions,impression penalty decay functions, maximum box traction decayfunctions, geometric progression decay functions, pay-to-play decayfunctions, and the like. Use of a decay function, for instance, ensuresthat old, no longer relevant links disappear naturally, therebydecreasing the chance that noise from things like random browsing willtrend up locally. Example applications of the various types of decayfunctions are described in further detail below.

In some embodiments, when making contextual content recommendations, therecommendation platform includes an affinity determination module 211 todetermine contextual distances between a potentially recommended contentitem a UE 109 or client 111 for which a recommendation is to be made. Inone embodiment, the contextual distances are dependent on the contextualparameter itself. For example, if the contextual parameter of interestis geolocation, the contextual distance corresponds to physicaldistances. Similarly, for time, the contextual distance is a differencein time-of-day, day-of-week, month, season, or any other time unit.Based, the contextual distances, the affinity determination module 211can adjust the traction applied specific content items to determinewhether to recommend the content items. For example, greater distancescan be used to reduce the traction applied between a content item andthe subject client 111. So, in this way, the closest items (e.g., basedon contextual distance) are recommended to the user.

Contextual distance or affinity determination may also be used inembodiments where content items are assigned to geographical boxes. Insome cases, presenting the user with suggested content items from aspecific box might prove to be inaccurate. For example, if the user islocated close to a border between adjacent boxes representing respectivegeographical areas, some content items in the adjacent box may actuallybe closer than content items in box in which the client 111 is located.Accordingly, in some embodiments, the affinity determination module 211may select to look for content items in adjacent boxes and/or may usecontextual distance calculations across adjacent boxes to provideinformation to the recommendation engine 213 to make the appropriaterecommendations.

In one embodiment, the recommendation engine 213 processes theaggregated popularity data (e.g., traction) associated with each contentitem based on the context of a particular client 111 to recommend themost relevant content items for the client's context. In one embodiment,the content usage patterns from a group of clients 111 are collected inreal-time or substantially real-time because many of the calculationsfor determining contexts and popularity data for the content items aremade “on-the-fly” while adding or updating a content item in the system100 based on access requests from the clients 111. In a proxy-basedsystem, the access requests are automatically detected by the proxyserver 103 and forwarded to the recommendation platform 101 forreal-time or near real-time processing. In non-proxy-based systems, theclients 111 may, for instance, send access requests or usage datadirectly to the recommendation platform 101 for processing (e.g., eitherperiodically or continuously).

In addition, because of the real-time nature of some embodiments, thesystem 100 can support both highly localized (e.g., based on location,time, and/or other contextual parameters) trend spikes as well ascontent items that are spread broadly but are very popular over largeranges, times, or other contextual distances.

FIG. 3 is a flowchart of a process for providing contextual contentsuggestions, according to one embodiment. In one embodiment, therecommendation platform 101 performs the process 300 and is implementedin, for instance, a chip set including a processor and a memory as shownFIG. 11. In addition or alternatively, the proxy server 103 may performall or a portion of the process 300. At step 301, the recommendationplatform 101 processes and/or facilitates a processing of contextualinformation associated with at least one device to determine one or morelocations, one or more contextual parameter values, or a combinationthereof. In one embodiment, the processing of the contextual informationis triggered when a client 111 accesses the proxy server 103, theservice platform 113, the services 115, the content provider 117, or anyother component of the system 100. In this example, the client 111 neednot make a specific request for any suggestions or recommendations.Instead, the recommendation platform 101 can automatically initiate theprocess 300 based using the system 100 to access content.

By way of example, the recommendation processes contextual data (e.g.,provided in an access request, reported to a server, inferred from theproxy server 103, etc.) associated with the client 111, the request, ora combination thereof to determine the context from which the client 111is accessing content over the system 100. In some embodiments, therecommendation platform 101 can process historical user data, preferenceinformation, calendar information, and the like to predict a futurecontext of the client 111. In one embodiment, the contextual data isprocessed to identify at least in part a location and/or othercontextual parameters (e.g., time, activity, intent, etc.) to describe acontext associated with the client 111.

The recommendation platform 101 then determines popularity dataassociated with one or more content items with respect to the one ormore locations, the one or more contextual parameter values, or acombination, wherein the popularity data is determined from one or moreother devices sharing at least substantially the one or more locations,the one or more contextual parameter values, or a combination thereof(step 303). As described above, the recommendation platform 101maintains records (e.g., access history database 107) of contentrequests made by a group of clients 111 that are associated with orstratified across one or more determined contexts.

In one embodiment, the recommendation platform 101 causes, at least inpart, a correlation of the one or locations to one or more boxes,wherein the one or more boxes represented respective one or more boundedgeographical areas; and wherein the popularity data is determined withrespect to the one or more boxes (step 305). For example, therecommendation platform 101 can use the contextual data of step 301 todetermine what geographic box the user is located in to facilitatelocalized recommendations or suggestions. In one embodiment, the sizesor boundaries of the box may be determined based, at least in part, howlocalized the recommendations should be. In other embodiments, the boxsizes can be determined dynamically based on the number of content itemsassigned to each box.

By way of example, the recommendation platform 101 determines thepopularity data based, at least in part, on a number of requests for theone or more content items by the at least one device, the one or otherdevices, or a combination thereof when sharing the at leastsubstantially the one or more locations, the one or more contextualparameter values, or a combination thereof (step 307). For example, asthe number of requests made for a content item increases so does itspopularity or traction. As noted previously, the recommendation platform101 can compute the popularity data or traction for content itemson-the-fly as requests for the content items are detected. In this way,the recommendation platform 101 has access to real-time or substantiallyreal-time data from which to make relevant recommendations orsuggestions.

In one embodiment, the recommendation platform 101 determine distanceinformation between the at least one device and the one or more contentinformation based, at least in part, on one or more distances between(a) the one or more item locations and the one or more locations of theclient 111; (b) the one or more item contextual parameters and the oneor more contextual parameters of the client 111; or (c) a combinationthereof, wherein the recommendation of the one or more content items isfurther based, at least in part, on the distance information (step 309).In one embodiment, the content items and the clients 111 are associatedwith respective locations or contexts. The differences between thelocations or contexts can be used to further refine the contextualcontent suggestion process. For example, distance can be used to weighpopularity values of nearby content items such that highly popular itemsthat are relatively far away, or very close content items are of lowerpopularity may still be recommended.

In step 311, the recommendation platform 101 causes, at least in part, arecommendation of the one or more content items to the at least onedevice based, at least in part, on the popularity information (e.g.,traction), the distance information (e.g., affinity), and/or otherrelated information. The recommendation can then be presented to theclient 111 with little to no user intervention.

FIG. 4 is a flowchart of a process for collecting content accesshistories to facilitate providing contextual content suggestions,according to one embodiment. In one embodiment, the recommendationplatform 101 performs the process 400 and is implemented in, forinstance, a chip set including a processor and a memory as shown FIG.11. In addition or alternatively, the proxy server 103 may perform allor a portion of the process 400. At step 401, the recommendationplatform collects content usage or access histories from a group of UEs109 or clients 111.

As previously discussed, when a client 111 requests access to oraccesses a content item (e.g., visits a URI), the content item is addedto the system, the recommendation platform determines or updates thegeoposition (P) or other contextual parameters (e.g., time, activity,intent) associated with the content item. In addition, therecommendation platform 101 computes or updates a popularity index ortraction for the content item.

In step 403, the recommendation platform causes, at least in part, anassociation of one or more item locations, one or more item contextualparameters, or a combination thereof with the one or more content itemsbased, at least in part, on the one or more locations, the one or morecontextual parameters, or a combination thereof associated with at leastone device, the one or more other devices, or a combination thereof whenrequesting the one or more content items. For example, when a URI isvisited by a web browser (e.g., a client 111), the recorded content itemis given an initial traction value and a respective geoposition isrecorded. As more users access the same content item the traction valueand/or geoposition can be updated accordingly.

In step 405, the recommendation platform 101 determines the popularitydata used in the process 300 of FIG. 3 based, at least in part, on thecontent access histories and associated contexts. In other words, therecommendation platform 101 logs content items that are popular among agroup of users sharing a context (e.g., a location, time, etc.). Thenwhen another client 111 accesses the system with the same or similarcontent, the recommendation platform 101 can determine which contentitems are most popular for the given context and make contextual contentsuggestions accordingly.

FIG. 5 is a flowchart of a process for top level domain filtering ofcontent access histories, according to one embodiment. In oneembodiment, the recommendation platform 101 performs the process 500 andis implemented in, for instance, a chip set including a processor and amemory as shown FIG. 11. In addition or alternatively, the proxy server103 may perform all or a portion of the process 500.

In one embodiment, when content items are accessed is accessed via URLs,the recommendation platform 101 can perform filtering of top leveldomains (TLDs) to avoid having gateway URLs trend up more in popularitythan destination URLs. For example, if four users load a news site andthen three of the users click on the main story and the fourth oneclicks on another story, the news site will trend up higher and fastersince four different users loaded it. However, it may be more relevantfor recommendations for the main story to be the one that trends up.Users might already know the news site, so a recommendation of the newsite as a content item may not be of interest to the users, whereas acertain news article from the site might be more interesting to moreusers.

Accordingly, in one embodiment, the recommendation platform 101processes and/or facilitates a processing of requests for one or morecontent items to determine one or more uniform resource locators (URLs)associated with the one or more content items (step 501). Therecommendation platform 101 can then determine the popularity data orwhether to record the content access request in the system 100 for thecontent items based, at least in part, whether the one or more URLs areone or more top level domains (step 503).

If the URL is not a TLD, then the recommendation platform 101 recordsthe URL as a content item in the system 100 (step 505). If the URL is aTLD, the recommendation platform 101 does not report the URLimmediately. Instead, the recommendation platform 101 waits for theuser's next request to determine whether the next request is a sub-URL(step 507). If the user's next request is a sub-URL of the previouslyrequested TLD, the top level domain is a sub-URL, then therecommendation platform 101 records the sub-URL as a content item in thesystem 100 (step 509).

If the next request is not a sub-URL of the previous TLD, therecommendation platform 101 determines whether the next request (e.g.,the request after the TLD request) is a non-TLD that is from acompletely different site or domain (step 511). If yes, therecommendation platform 101 records both the TLD and the new request ascontent items in the system 100 (step 513).

If no, the recommendation platform 101 determines with the new requestis a TLD, but different from the previous TLD request (step 515). Ifyes, the recommendation platform 101 returns to step 507 to repeat theprocess with the new TLD.

If the user stops using the browser (e.g., when the user session expiresin the proxy server 103) (step 517), then the recommendation platform101 determines whether it was waiting to decide whether or not report aURL to the system 100. If the recommendation platform 101 was waiting,then the recommendation platform 101 will report or record whatever URLwas waiting the queue as a content item in the system 100.

In one embodiment, the recommendation platform 101 can also distinguishbetween an organic URL request versus a click-through URL request whenreporting content items to the system 100. By way of example, an organicURL request is one which was generated using standard browsing—e.g., theURL was entered in the address bar of a browser or client application111 or through a link that is not displayed as part of list of contentitems recommended by the system 100. On the other hand, a click-throughURL request is one which was generated by clicking on a link recommendedby the system 100.

In one embodiment, the system 100 supports the ability to add differenttraction amounts to URLs depending on whether they are organic orclick-through request. Both traction amounts, for instance, areconfigurable in the system 100. Usually, the click-through request for aURL will have a lower traction added to that URL than for an organicrequest.

FIG. 6 is a flowchart of a process for decaying popularity data used fordetermining contextual content suggestions, according to one embodiment.In one embodiment, the recommendation platform 101 performs the process600 and is implemented in, for instance, a chip set including aprocessor and a memory as shown FIG. 11. In addition or alternatively,the proxy server 103 may perform all or a portion of the process 600.

In order to effectively maintain a set of content items (e.g., URIs) inthe system 100 that are relevant over time, the recommendation platformuses various methods for reducing the popularity of URIs as they gounused overtime. Otherwise, there is the potential to have an evergrowing amount of overall traction or popularity in the system 100.Accordingly, in one embodiment, the recommendation platform 101 causes,at least in part, a decay of the popularity information using, forinstance, one or more decay methods (step 601).

In one embodiment, the recommendation platform 101 uses a decay functionthat is based on an impression penalty. In other words, therecommendation platform 101 determines the decay of the popularity databased, at least in part, on whether the at least one device requestsaccess to the one or more content items in response to a recommendationof the one or more content items (step 603). For example, an impressionrefers to when a client 111 is presented with a recommended content itemin the client 111's recommended list. In this case, the user sees therecommended content item in the recommended list, but does notnecessarily click on or otherwise access the recommended content item.In one embodiment, the impression penalty mechanism penalizes contentitems that reside in the recommended list without being requested oraccessed by the user. Content items typically get a benefit from makingit onto the recommended list in the form of exposure to the user. Thisexposure often increases the likelihood of the content item beingaccessed, thus increasing the content item's traction or popularity.However, if a link fails to be accessed by the user, it suggests thatthe content item might not be interesting to users and should be removedfrom the recommended list faster than content items that do getaccessed. Consequently, the recommendation platform 101 uses theimpression penalty decay to facilitate this process.

In one embodiment, the impression penalty decay function reduces theamount of traction for content items that are presented in a recommendedlist for the user to see. The content items that are qualified to be onthe recommended list, but are not actually presented to the user are notpenalized under this decay function. Each content item in therecommended list has its traction decreased by a configurable amountbefore the system 100 responds to a client with a new or updated contentrecommendation list. In one embodiment, the amount that is decreased isequal for all content items in the recommended list, but can beconfigured to be different depending on where the content item ranks inthe list. For example, the content item that is first in the recommendedlist (e.g., that a user will see) can have its impression penalty behigher than for a content item that is last in the recommended list.

In other embodiments, the recommendation platform 101 uses a decayfunction based, at least in part, on one or more temporal parameters, ora combination thereof (step 605). More specifically, the recommendationplatform 101, in one embodiment, can apply a time-based decay functionon each content item, so that as time elapses, the traction for contentitems will decrease. The decay function can be linear, exponential, orsome other mode.

In step 607, the recommendation platform 101 recommends content itemsbased, at least in part, the popularity data with decay applied via theone or more decay functions described above in combination with thedistance and other affinity determination mechanisms previouslydescribed.

FIG. 7 is a flowchart of a process for managing popularity data decay byspecifying maximum popularity values, according to one embodiment. Inone embodiment, the recommendation platform 101 performs the process 700and is implemented in, for instance, a chip set including a processorand a memory as shown FIG. 11. In addition or alternatively, the proxyserver 103 may perform all or a portion of the process 700.

At step 701, the recommendation platform 101 causes, at least in part, acorrelation of the one or locations associated with one or more contentitems to one or more boxes. As describe previously, in one embodiment,the one or more boxes represented respective one or more boundedgeographical areas. The recommendation platform 101 then determinespopularity data for the content items with respect to the boxes.

In step 703, the recommendation platform 101 determines a maximumpopularity value for the one or more boxes. In other words, therecommendation platform 101 manages traction decay for content items byimposing a maximum total amount of traction for a given geographic box.In this model, each box is assigned a maximum total value that it cancontain as computed by, for instance, the sum of the traction of allcontent items in the given box. There are several approaches formanaging maximum box traction (see discussion below with respect to FIG.8).

The recommendation platform 101 then determines the popularity databased, at least in part, on a distribution of the maximum popularityvalue among the one or more content items associated with the respectiveone or more boxes (step 705).

FIG. 8 is a diagram depicting a decay of popularity data via geometricprogression, according to one embodiment. In one embodiment, the decayfunction applies the maximum box traction decay function using themathematical concept of geometric progression. In one embodiment, eachgeographical box would have a fixed maximum traction value (Tmax). OnceTmax is reach, the box would need to get traction for new content itemscoming into the box. In one embodiment, the way to acquire new tractionwould be to deduct a constant proportion (e.g., a third, a half) of thetraction from each content item already existing in the box. This way,the entire box now has a constant proportion of Tmax traction to give tonew incoming content items (e.g., content items assigned to the box). Inthis scheme, content items could not go above a maximum individualtraction. Also, there would be a minimum individual fraction thresholdthat would cause content items to be deleted from the box once they fallbelow the minimum threshold, and their remaining traction would be giveback to the box.

Using this scenario and assuming a content item does not acquire newtraction, a content item's traction would follow a geometric progressionwhen decaying. FIG. 8 depicts an example of a system where the minimumtraction for a content item or URL is 30 and where the constant decayproportion is half (50%). As shown, before the first decay 801, thetraction for the content item (e.g., a URL) is 320. After the firstdecay 803, this particular content item gives 160 traction points backto the box. After the second decay 805, the content item gives 80traction points back to the box. After the third decay 807, the contentitem gives 40 traction points back to the box. After the fourth decay809, the content item gives all of its remaining traction (e.g., 40)back to the box. Moreover, because the after the fourth decay 809, thetraction falls below the minimum individual threshold level of 30, therecommendation platform 101 deletes the content time from the box andreturns any remaining traction to box.

As discussed above, geometric decay is one possible embodiment ofmaximum total traction decay function. In another embodiment, therecommendation system can use a pay-to-play function. For example, a boxis assumed to have a configurable maxim traction as shown in Table 1below.

TABLE 1

In one embodiment, every time, the recommendation platform adds a newcontent item to the box, the recommendation platform 101 takes from theavailable traction (e.g., needs 100 fraction points every time a newcontent item is inserted or updated in the box). In this example, a lastaccess time (LAT) contextual parameter is also recorded with eachcontent item. An example is provided below in Table 2.

TABLE 2

In this example, some of the content items (e.g., URLs) are visited moretimes. Accordingly, the recommendation platform 101 modifies theirtraction and LAT. Also, a new content item is added (e.g.,www.yahoo.com). The resulting traction allocation is provided in Table 3below.

TABLE 3

At this point there is not more space or available traction points. Inthis scenario, the user now accesses a new content item (e.g.,www.nytimes.com), and the recommendation platform 101 inserts the newcontent item in the box as shown in Table 4 below.

TABLE 4

At this point the box is beyond its traction capacity (capacity is 2000,current total traction 2100), so the recommendation platform 101 needsto release some traction. In one embodiment, the recommendation platform101 looks for the content items that have not been accessed in thelongest time. In the example above, it would be links like MSN,Facebook, BBC, etc. The main point of this decay function is to penalizeURLs that are no longer being actively requested by users, so therecommendation platform 101 takes some configurable amount of tractionfrom each of them to obtain the necessary 100 traction points tonormalize the box back to its allowed total traction. In order toachieve this, the recommendation platform takes a configurable amount oftraction from the content items that have not been accessed in thelongest time. In this example, the recommendation platform 101 takes 20traction points as penalty from these content items as shown in Table 5below.

TABLE 5

The Table 5 above has been normalized to its maximum allowed traction.The content items from which the recommendation platform 101 took thetraction points from have now begun to decay. In one embodiment, everytime a process acts on the content items, the recommendation platform101 updates the corresponding LAT. So, in the case above, because therecommendation platform 101 had to give up some of their traction, therecommendation platform 101 updates their LAT and moves them to the topof the table as shown in Table 6 below.

TABLE 6

In the above example, if the content items that gave some of theirtraction up are not clicked on or loaded again but other content itemsare, they will once again become the oldest content items. Accordingly,when traction is needed, they will have to give up more of theirtraction until they eventually reach a minimum traction allowed and areremoved from the box.

FIGS. 9A-9D are diagrams of depicting an overview of providingcontextual content recommendations, according to various embodiments.FIG. 9A depicts an example in which a UE 109 a requests access to acontent item 901 with a location context defined by geographical box903. As previously described, when a UE 109 a or a client 111 executingin the UE 109 a accesses or requests access to a content item 901, therecommendation platform 101 records the geoposition (P=p₀) of therequest and assigns an initial traction value (T=t_(o)) to the contentitem 901. In the example of FIG. 9A, the height of the icon representingthe content item 901 represents the traction assigned.

In one embodiment, an access request for a URI is based on visiting theURI. By way of example, a visit constitutes any way a client 111 canload a URI (e.g., directly typed in, from favorites, a link on anotherpage, reference from another application, etc.). In addition to the keyparameters (e.g., P and T), the recommendation platform 101 can alsorecord the time the content was added, the URI title, any icon, textualdescription, and/or any other contextual parameter associated with therequest. Additionally, the system 100 may keep a static image of thecontent item as it appeared at the time of the visit. Content itemsadded to the system 100 in this way are referred to as crowdsourcedlinks.

As shown in FIG. 9B, if the same content item 901 is accessed by anotherUE 109 b in the same box 903 or other contextual parameter, the contentitem 901's position and traction will be adjusted. For example, theprevious traction of the content item 901 will be incremented by a setvalue t_(n) to reflect the increased popularity (e.g.,T=T_(prev)+T_(n)). The increased popularity is depicted in FIG. 9B as anincrease in height of the icon representing the content item 901.Additionally, the position of the content item 901 will be adjusted bycomputing the “center of mass” between the previous position of thecontent item (e.g., the position of the UE 109 a when accessing thecontent item 901) and the new position (e.g., the position of the UE 109b when accessing the content item 901) via, for instance, the equation:p_(n): P=(p_(n)t_(n)+P_(prev)T_(prev))/T.

As shown in FIG. 9C, in addition to adding content items via thecrowdsourcing method, a content item can be promoted into the systemwith chosen position and initial traction. The traction value of thepromoted link is configurable and can be larger than the default valuegiven by a visited link entered into the system. For example, promotedcontent item 911 in FIG. 9C is defined at a particular location withgreater traction than content item 901.

As shown in FIG. 9D, a UE 109 c can request or be provided with a listof suggested content items from the recommendation platform 101. In oneembodiment, the UE 109 c can report its geolocation or other contextualparameters to the recommendation platform 101. The recommendationplatform 101 then uses finds the best n matches or suggestions (where nis a preconfigured number). To find the best matches, the recommendationplatform 101 computes an affinity (A) of each content item. In someembodiments, the affinity is based, at least in part, on the contextualdistances discussed previously. In one embodiment, affinity isproportional to the traction of each content item and inverselyproportional to the contextual distance (e.g., physical distance betweenthe content item position and the position or predicted position of theUE 109 c). In one embodiment, the relationship among the affinity, thetraction, and/or the contextual distance can be linear, quadratic, orany other function that falls off over distance including discretesteps.

In one embodiment, to make a recommendation of content items, therecommendation platform 101 first looks at content items (e.g., contentitems 901 and 911) in the box 903 containing the requesting UE 109 c. Ifthe box 903 does not contain enough matches, the recommendation platform101 can follow one over several strategies discussed above (e.g., takingcontent items from adjacent boxes, promoting content items, etc.).

FIG. 10 is a diagram of a user interface reflecting the processes ofFIGS. 1-9, according to one embodiment. As shown, a user interface 1001presents a list 1003 of content items 1005-1009 that are determinedusing the various embodiments described herein. For example, a user canlaunch a client 111 to request contextual content suggestions from therecommendation platform 101. In one embodiment, the user need not makean explicit request for the suggestions other than opening or otheraccessing a service associated with the recommendation platform 101. Onaccessing the service, the client 111 can be configured to report itslocation and/or other contextual parameters (e.g., time, activity,intent, etc.) to recommendation platform 101.

Based on the reported contextual parameters, the recommendation platform101 can search for content items that are most popular among otherclients 111 for the given context. In this example, based on the contextof the client 111, the recommendation platform 101 has presented contentitems 1005-1009 as the most popular among users sharing the same context(e.g., at the same location and time). If the user views the presentedcontent items 1005-1009 but does not access or click on any of thesuggestions, the recommendation platform can apply one or more decayfunctions (e.g., impression penalty decay) as described above. In thisway, the user associated with the client 111 can be presented withdifferent suggestions when another recommendation list is provided.

The processes described herein for providing contextual contentsuggestion may be advantageously implemented via software, hardware,firmware or a combination of software and/or firmware and/or hardware.For example, the processes described herein, may be advantageouslyimplemented via processor(s), Digital Signal Processing (DSP) chip, anApplication Specific Integrated Circuit (ASIC), Field Programmable GateArrays (FPGAs), etc. Such exemplary hardware for performing thedescribed functions is detailed below.

FIG. 11 illustrates a computer system 1100 upon which an embodiment ofthe invention may be implemented. Although computer system 1100 isdepicted with respect to a particular device or equipment, it iscontemplated that other devices or equipment (e.g., network elements,servers, etc.) within FIG. 11 can deploy the illustrated hardware andcomponents of system 1100. Computer system 1100 is programmed (e.g., viacomputer program code or instructions) to provide contextual contentsuggestion as described herein and includes a communication mechanismsuch as a bus 1110 for passing information between other internal andexternal components of the computer system 1100. Information (alsocalled data) is represented as a physical expression of a measurablephenomenon, typically electric voltages, but including, in otherembodiments, such phenomena as magnetic, electromagnetic, pressure,chemical, biological, molecular, atomic, sub-atomic and quantuminteractions. For example, north and south magnetic fields, or a zeroand non-zero electric voltage, represent two states (0, 1) of a binarydigit (bit). Other phenomena can represent digits of a higher base. Asuperposition of multiple simultaneous quantum states before measurementrepresents a quantum bit (qubit). A sequence of one or more digitsconstitutes digital data that is used to represent a number or code fora character. In some embodiments, information called analog data isrepresented by a near continuum of measurable values within a particularrange. Computer system 1100, or a portion thereof, constitutes a meansfor performing one or more steps of providing contextual contentsuggestion.

A bus 1110 includes one or more parallel conductors of information sothat information is transferred quickly among devices coupled to the bus1110. One or more processors 1102 for processing information are coupledwith the bus 1110.

A processor (or multiple processors) 1102 performs a set of operationson information as specified by computer program code related toproviding contextual content suggestion. The computer program code is aset of instructions or statements providing instructions for theoperation of the processor and/or the computer system to performspecified functions. The code, for example, may be written in a computerprogramming language that is compiled into a native instruction set ofthe processor. The code may also be written directly using the nativeinstruction set (e.g., machine language). The set of operations includebringing information in from the bus 1110 and placing information on thebus 1110. The set of operations also typically include comparing two ormore units of information, shifting positions of units of information,and combining two or more units of information, such as by addition ormultiplication or logical operations like OR, exclusive OR (XOR), andAND. Each operation of the set of operations that can be performed bythe processor is represented to the processor by information calledinstructions, such as an operation code of one or more digits. Asequence of operations to be executed by the processor 1102, such as asequence of operation codes, constitute processor instructions, alsocalled computer system instructions or, simply, computer instructions.Processors may be implemented as mechanical, electrical, magnetic,optical, chemical or quantum components, among others, alone or incombination.

Computer system 1100 also includes a memory 1104 coupled to bus 1110.The memory 1104, such as a random access memory (RAM) or any otherdynamic storage device, stores information including processorinstructions for providing contextual content suggestion. Dynamic memoryallows information stored therein to be changed by the computer system1100. RAM allows a unit of information stored at a location called amemory address to be stored and retrieved independently of informationat neighboring addresses. The memory 1104 is also used by the processor1102 to store temporary values during execution of processorinstructions. The computer system 1100 also includes a read only memory(ROM) 1106 or any other static storage device coupled to the bus 1110for storing static information, including instructions, that is notchanged by the computer system 1100. Some memory is composed of volatilestorage that loses the information stored thereon when power is lost.Also coupled to bus 1110 is a non-volatile (persistent) storage device1108, such as a magnetic disk, optical disk or flash card, for storinginformation, including instructions, that persists even when thecomputer system 1100 is turned off or otherwise loses power.

Information, including instructions for providing contextual contentsuggestion, is provided to the bus 1110 for use by the processor from anexternal input device 1112, such as a keyboard containing alphanumerickeys operated by a human user, or a sensor. A sensor detects conditionsin its vicinity and transforms those detections into physical expressioncompatible with the measurable phenomenon used to represent informationin computer system 1100. Other external devices coupled to bus 1110,used primarily for interacting with humans, include a display device1114, such as a cathode ray tube (CRT), a liquid crystal display (LCD),a light emitting diode (LED) display, an organic LED (OLED) display, aplasma screen, or a printer for presenting text or images, and apointing device 1116, such as a mouse, a trackball, cursor directionkeys, or a motion sensor, for controlling a position of a small cursorimage presented on the display 1114 and issuing commands associated withgraphical elements presented on the display 1114. In some embodiments,for example, in embodiments in which the computer system 1100 performsall functions automatically without human input, one or more of externalinput device 1112, display device 1114 and pointing device 1116 isomitted.

In the illustrated embodiment, special purpose hardware, such as anapplication specific integrated circuit (ASIC) 1120, is coupled to bus1110. The special purpose hardware is configured to perform operationsnot performed by processor 1102 quickly enough for special purposes.Examples of ASICs include graphics accelerator cards for generatingimages for display 1114, cryptographic boards for encrypting anddecrypting messages sent over a network, speech recognition, andinterfaces to special external devices, such as robotic arms and medicalscanning equipment that repeatedly perform some complex sequence ofoperations that are more efficiently implemented in hardware.

Computer system 1100 also includes one or more instances of acommunications interface 1170 coupled to bus 1110. Communicationinterface 1170 provides a one-way or two-way communication coupling to avariety of external devices that operate with their own processors, suchas printers, scanners and external disks. In general the coupling iswith a network link 1178 that is connected to a local network 1180 towhich a variety of external devices with their own processors areconnected. For example, communication interface 1170 may be a parallelport or a serial port or a universal serial bus (USB) port on a personalcomputer. In some embodiments, communications interface 1170 is anintegrated services digital network (ISDN) card or a digital subscriberline (DSL) card or a telephone modem that provides an informationcommunication connection to a corresponding type of telephone line. Insome embodiments, a communication interface 1170 is a cable modem thatconverts signals on bus 1110 into signals for a communication connectionover a coaxial cable or into optical signals for a communicationconnection over a fiber optic cable. As another example, communicationsinterface 1170 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN, such as Ethernet. Wirelesslinks may also be implemented. For wireless links, the communicationsinterface 1170 sends or receives or both sends and receives electrical,acoustic or electromagnetic signals, including infrared and opticalsignals, that carry information streams, such as digital data. Forexample, in wireless handheld devices, such as mobile telephones likecell phones, the communications interface 1170 includes a radio bandelectromagnetic transmitter and receiver called a radio transceiver. Incertain embodiments, the communications interface 1170 enablesconnection to the communication network 105 for providing contextualcontent suggestion to the UE 101.

The term “computer-readable medium” as used herein refers to any mediumthat participates in providing information to processor 1102, includinginstructions for execution. Such a medium may take many forms,including, but not limited to computer-readable storage medium (e.g.,non-volatile media, volatile media), and transmission media.Non-transitory media, such as non-volatile media, include, for example,optical or magnetic disks, such as storage device 1108. Volatile mediainclude, for example, dynamic memory 1104. Transmission media include,for example, twisted pair cables, coaxial cables, copper wire, fiberoptic cables, and carrier waves that travel through space without wiresor cables, such as acoustic waves and electromagnetic waves, includingradio, optical and infrared waves. Signals include man-made transientvariations in amplitude, frequency, phase, polarization or otherphysical properties transmitted through the transmission media. Commonforms of computer-readable media include, for example, a floppy disk, aflexible disk, hard disk, magnetic tape, any other magnetic medium, aCD-ROM, CDRW, DVD, any other optical medium, punch cards, paper tape,optical mark sheets, any other physical medium with patterns of holes orother optically recognizable indicia, a RAM, a PROM, an EPROM, aFLASH-EPROM, an EEPROM, a flash memory, any other memory chip orcartridge, a carrier wave, or any other medium from which a computer canread. The term computer-readable storage medium is used herein to referto any computer-readable medium except transmission media.

Logic encoded in one or more tangible media includes one or both ofprocessor instructions on a computer-readable storage media and specialpurpose hardware, such as ASIC 1120.

Network link 1178 typically provides information communication usingtransmission media through one or more networks to other devices thatuse or process the information. For example, network link 1178 mayprovide a connection through local network 1180 to a host computer 1182or to equipment 1184 operated by an Internet Service Provider (ISP). ISPequipment 1184 in turn provides data communication services through thepublic, world-wide packet-switching communication network of networksnow commonly referred to as the Internet 1190.

A computer called a server host 1192 connected to the Internet hosts aprocess that provides a service in response to information received overthe Internet. For example, server host 1192 hosts a process thatprovides information representing video data for presentation at display1114. It is contemplated that the components of system 1100 can bedeployed in various configurations within other computer systems, e.g.,host 1182 and server 1192.

At least some embodiments of the invention are related to the use ofcomputer system 1100 for implementing some or all of the techniquesdescribed herein. According to one embodiment of the invention, thosetechniques are performed by computer system 1100 in response toprocessor 1102 executing one or more sequences of one or more processorinstructions contained in memory 1104. Such instructions, also calledcomputer instructions, software and program code, may be read intomemory 1104 from another computer-readable medium such as storage device1108 or network link 1178. Execution of the sequences of instructionscontained in memory 1104 causes processor 1102 to perform one or more ofthe method steps described herein. In alternative embodiments, hardware,such as ASIC 1120, may be used in place of or in combination withsoftware to implement the invention. Thus, embodiments of the inventionare not limited to any specific combination of hardware and software,unless otherwise explicitly stated herein.

The signals transmitted over network link 1178 and other networksthrough communications interface 1170, carry information to and fromcomputer system 1100. Computer system 1100 can send and receiveinformation, including program code, through the networks 1180, 1190among others, through network link 1178 and communications interface1170. In an example using the Internet 1190, a server host 1192transmits program code for a particular application, requested by amessage sent from computer 1100, through Internet 1190, ISP equipment1184, local network 1180 and communications interface 1170. The receivedcode may be executed by processor 1102 as it is received, or may bestored in memory 1104 or in storage device 1108 or any othernon-volatile storage for later execution, or both. In this manner,computer system 1100 may obtain application program code in the form ofsignals on a carrier wave.

Various forms of computer readable media may be involved in carrying oneor more sequence of instructions or data or both to processor 1102 forexecution. For example, instructions and data may initially be carriedon a magnetic disk of a remote computer such as host 1182. The remotecomputer loads the instructions and data into its dynamic memory andsends the instructions and data over a telephone line using a modem. Amodem local to the computer system 1100 receives the instructions anddata on a telephone line and uses an infra-red transmitter to convertthe instructions and data to a signal on an infra-red carrier waveserving as the network link 1178. An infrared detector serving ascommunications interface 1170 receives the instructions and data carriedin the infrared signal and places information representing theinstructions and data onto bus 1110. Bus 1110 carries the information tomemory 1104 from which processor 1102 retrieves and executes theinstructions using some of the data sent with the instructions. Theinstructions and data received in memory 1104 may optionally be storedon storage device 1108, either before or after execution by theprocessor 1102.

FIG. 12 illustrates a chip set or chip 1200 upon which an embodiment ofthe invention may be implemented. Chip set 1200 is programmed to providecontextual content suggestion as described herein and includes, forinstance, the processor and memory components described with respect toFIG. 11 incorporated in one or more physical packages (e.g., chips). Byway of example, a physical package includes an arrangement of one ormore materials, components, and/or wires on a structural assembly (e.g.,a baseboard) to provide one or more characteristics such as physicalstrength, conservation of size, and/or limitation of electricalinteraction. It is contemplated that in certain embodiments the chip set1200 can be implemented in a single chip. It is further contemplatedthat in certain embodiments the chip set or chip 1200 can be implementedas a single “system on a chip.” It is further contemplated that incertain embodiments a separate ASIC would not be used, for example, andthat all relevant functions as disclosed herein would be performed by aprocessor or processors. Chip set or chip 1200, or a portion thereof,constitutes a means for performing one or more steps of providing userinterface navigation information associated with the availability offunctions. Chip set or chip 1200, or a portion thereof, constitutes ameans for performing one or more steps of providing contextual contentsuggestion.

In one embodiment, the chip set or chip 1200 includes a communicationmechanism such as a bus 1201 for passing information among thecomponents of the chip set 1200. A processor 1203 has connectivity tothe bus 1201 to execute instructions and process information stored in,for example, a memory 1205. The processor 1203 may include one or moreprocessing cores with each core configured to perform independently. Amulti-core processor enables multiprocessing within a single physicalpackage. Examples of a multi-core processor include two, four, eight, orgreater numbers of processing cores. Alternatively or in addition, theprocessor 1203 may include one or more microprocessors configured intandem via the bus 1201 to enable independent execution of instructions,pipelining, and multithreading. The processor 1203 may also beaccompanied with one or more specialized components to perform certainprocessing functions and tasks such as one or more digital signalprocessors (DSP) 1207, or one or more application-specific integratedcircuits (ASIC) 1209. A DSP 1207 typically is configured to processreal-world signals (e.g., sound) in real time independently of theprocessor 1203. Similarly, an ASIC 1209 can be configured to performedspecialized functions not easily performed by a more general purposeprocessor. Other specialized components to aid in performing theinventive functions described herein may include one or more fieldprogrammable gate arrays (FPGA) (not shown), one or more controllers(not shown), or one or more other special-purpose computer chips.

In one embodiment, the chip set or chip 1200 includes merely one or moreprocessors and some software and/or firmware supporting and/or relatingto and/or for the one or more processors.

The processor 1203 and accompanying components have connectivity to thememory 1205 via the bus 1201. The memory 1205 includes both dynamicmemory (e.g., RAM, magnetic disk, writable optical disk, etc.) andstatic memory (e.g., ROM, CD-ROM, etc.) for storing executableinstructions that when executed perform the inventive steps describedherein to provide contextual content suggestion. The memory 1205 alsostores the data associated with or generated by the execution of theinventive steps.

FIG. 13 is a diagram of exemplary components of a mobile terminal (e.g.,handset) for communications, which is capable of operating in the systemof FIG. 1, according to one embodiment. In some embodiments, mobileterminal 1301, or a portion thereof, constitutes a means for performingone or more steps of providing contextual content suggestion. Generally,a radio receiver is often defined in terms of front-end and back-endcharacteristics. The front-end of the receiver encompasses all of theRadio Frequency (RF) circuitry whereas the back-end encompasses all ofthe base-band processing circuitry. As used in this application, theterm “circuitry” refers to both: (1) hardware-only implementations (suchas implementations in only analog and/or digital circuitry), and (2) tocombinations of circuitry and software (and/or firmware) (such as, ifapplicable to the particular context, to a combination of processor(s),including digital signal processor(s), software, and memory(ies) thatwork together to cause an apparatus, such as a mobile phone or server,to perform various functions). This definition of “circuitry” applies toall uses of this term in this application, including in any claims. As afurther example, as used in this application and if applicable to theparticular context, the term “circuitry” would also cover animplementation of merely a processor (or multiple processors) and its(or their) accompanying software/or firmware. The term “circuitry” wouldalso cover if applicable to the particular context, for example, abaseband integrated circuit or applications processor integrated circuitin a mobile phone or a similar integrated circuit in a cellular networkdevice or other network devices.

Pertinent internal components of the telephone include a Main ControlUnit (MCU) 1303, a Digital Signal Processor (DSP) 1305, and areceiver/transmitter unit including a microphone gain control unit and aspeaker gain control unit. A main display unit 1307 provides a displayto the user in support of various applications and mobile terminalfunctions that perform or support the steps of providing contextualcontent suggestion. The display 1307 includes display circuitryconfigured to display at least a portion of a user interface of themobile terminal (e.g., mobile telephone). Additionally, the display 1307and display circuitry are configured to facilitate user control of atleast some functions of the mobile terminal. An audio function circuitry1309 includes a microphone 1311 and microphone amplifier that amplifiesthe speech signal output from the microphone 1311. The amplified speechsignal output from the microphone 1311 is fed to a coder/decoder (CODEC)1313.

A radio section 1315 amplifies power and converts frequency in order tocommunicate with a base station, which is included in a mobilecommunication system, via antenna 1317. The power amplifier (PA) 1319and the transmitter/modulation circuitry are operationally responsive tothe MCU 1303, with an output from the PA 1319 coupled to the duplexer1321 or circulator or antenna switch, as known in the art. The PA 1319also couples to a battery interface and power control unit 1320.

In use, a user of mobile terminal 1301 speaks into the microphone 1311and his or her voice along with any detected background noise isconverted into an analog voltage. The analog voltage is then convertedinto a digital signal through the Analog to Digital Converter (ADC)1323. The control unit 1303 routes the digital signal into the DSP 1305for processing therein, such as speech encoding, channel encoding,encrypting, and interleaving. In one embodiment, the processed voicesignals are encoded, by units not separately shown, using a cellulartransmission protocol such as enhanced data rates for global evolution(EDGE), general packet radio service (GPRS), global system for mobilecommunications (GSM), Internet protocol multimedia subsystem (IMS),universal mobile telecommunications system (UMTS), etc., as well as anyother suitable wireless medium, e.g., microwave access (WiMAX), LongTerm Evolution (LTE) networks, code division multiple access (CDMA),wideband code division multiple access (WCDMA), wireless fidelity(WiFi), satellite, and the like, or any combination thereof.

The encoded signals are then routed to an equalizer 1325 forcompensation of any frequency-dependent impairments that occur duringtransmission though the air such as phase and amplitude distortion.After equalizing the bit stream, the modulator 1327 combines the signalwith a RF signal generated in the RF interface 1329. The modulator 1327generates a sine wave by way of frequency or phase modulation. In orderto prepare the signal for transmission, an up-converter 1331 combinesthe sine wave output from the modulator 1327 with another sine wavegenerated by a synthesizer 1333 to achieve the desired frequency oftransmission. The signal is then sent through a PA 1319 to increase thesignal to an appropriate power level. In practical systems, the PA 1319acts as a variable gain amplifier whose gain is controlled by the DSP1305 from information received from a network base station. The signalis then filtered within the duplexer 1321 and optionally sent to anantenna coupler 1335 to match impedances to provide maximum powertransfer. Finally, the signal is transmitted via antenna 1317 to a localbase station. An automatic gain control (AGC) can be supplied to controlthe gain of the final stages of the receiver. The signals may beforwarded from there to a remote telephone which may be another cellulartelephone, any other mobile phone or a land-line connected to a PublicSwitched Telephone Network (PSTN), or other telephony networks.

Voice signals transmitted to the mobile terminal 1301 are received viaantenna 1317 and immediately amplified by a low noise amplifier (LNA)1337. A down-converter 1339 lowers the carrier frequency while thedemodulator 1341 strips away the RF leaving only a digital bit stream.The signal then goes through the equalizer 1325 and is processed by theDSP 1305. A Digital to Analog Converter (DAC) 1343 converts the signaland the resulting output is transmitted to the user through the speaker1345, all under control of a Main Control Unit (MCU) 1303 which can beimplemented as a Central Processing Unit (CPU) (not shown).

The MCU 1303 receives various signals including input signals from thekeyboard 1347. The keyboard 1347 and/or the MCU 1303 in combination withother user input components (e.g., the microphone 1311) comprise a userinterface circuitry for managing user input. The MCU 1303 runs a userinterface software to facilitate user control of at least some functionsof the mobile terminal 1301 to provide contextual content suggestion.The MCU 1303 also delivers a display command and a switch command to thedisplay 1307 and to the speech output switching controller,respectively. Further, the MCU 1303 exchanges information with the DSP1305 and can access an optionally incorporated SIM card 1349 and amemory 1351. In addition, the MCU 1303 executes various controlfunctions required of the terminal. The DSP 1305 may, depending upon theimplementation, perform any of a variety of conventional digitalprocessing functions on the voice signals. Additionally, DSP 1305determines the background noise level of the local environment from thesignals detected by microphone 1311 and sets the gain of microphone 1311to a level selected to compensate for the natural tendency of the userof the mobile terminal 1301.

The CODEC 1313 includes the ADC 1323 and DAC 1343. The memory 1351stores various data including call incoming tone data and is capable ofstoring other data including music data received via, e.g., the globalInternet. The software module could reside in RAM memory, flash memory,registers, or any other form of writable storage medium known in theart. The memory device 1351 may be, but not limited to, a single memory,CD, DVD, ROM, RAM, EEPROM, optical storage, magnetic disk storage, flashmemory storage, or any other non-volatile storage medium capable ofstoring digital data.

An optionally incorporated SIM card 1349 carries, for instance,important information, such as the cellular phone number, the carriersupplying service, subscription details, and security information. TheSIM card 1349 serves primarily to identify the mobile terminal 1301 on aradio network. The card 1349 also contains a memory for storing apersonal telephone number registry, text messages, and user specificmobile terminal settings.

While the invention has been described in connection with a number ofembodiments and implementations, the invention is not so limited butcovers various obvious modifications and equivalent arrangements, whichfall within the purview of the appended claims. Although features of theinvention are expressed in certain combinations among the claims, it iscontemplated that these features can be arranged in any combination andorder.

1. A method comprising facilitating a processing of and/or processing(1) data and/or (2) information and/or (3) at least one signal, the (1)data and/or (2) information and/or (3) at least one signal based, atleast in part, on the following: a processing of contextual informationassociated with at least one device to determine one or more locations,one or more contextual parameter values, or a combination thereof; atleast one determination of popularity data associated with one or morecontent items with respect to the one or more locations, the one or morecontextual parameter values, or a combination, wherein the popularitydata is determined from one or more other devices sharing at leastsubstantially the one or more locations, the one or more contextualparameter values, or a combination thereof; and a recommendation of theone or more content items to the at least one device based, at least inpart, on the popularity information.
 2. A method of claim 1, wherein the(1) data and/or (2) information and/or (3) at least one signal arefurther based, at least in part, on the following: at least onedetermination of the popularity data based, at least in part, on anumber of requests for the one or more content items by the at least onedevice, the one or other devices, or a combination thereof when sharingthe at least substantially the one or more locations, the one or morecontextual parameter values, or a combination thereof.
 3. A method ofclaim 2, wherein the (1) data and/or (2) information and/or (3) at leastone signal are further based, at least in part, on the following: aprocessing of the requests to determine one or more uniform resourcelocators (URLs) associated with the one or more content items; and atleast one determination of the popularity data based, at least in part,on whether the one or more URLs are one or more top level domains.
 4. Amethod of claim 1, wherein the (1) data and/or (2) information and/or(3) at least one signal are further based, at least in part, on thefollowing: an association of one or more item locations, one or moreitem contextual parameters, or a combination thereof with the one ormore content items based, at least in part, on the one or morelocations, the one or more contextual parameters, or a combinationthereof associated with at least one device, the one or more otherdevices, or a combination thereof when requesting the one or morecontent items.
 5. A method of claim 4, wherein the (1) data and/or (2)information and/or (3) at least one signal are further based, at leastin part, on the following: at least one determination of distanceinformation between the at least one device and the one or more contentinformation based, at least in part, on one or more distances between(a) the one or more item locations and the one or more locations; (b)the one or more item contextual parameters and the one or morecontextual parameters; or (c) a combination thereof, wherein therecommendation of the one or more content items is further based, atleast in part, on the distance information.
 6. A method of claim 1,wherein the (1) data and/or (2) information and/or (3) at least onesignal are further based, at least in part, on the following: a decay ofthe popularity information.
 7. A method of claim 6, wherein the decay isbased, at least in part, on one or more temporal parameters, one or moregeometric progressions, or a combination thereof.
 8. A method of claim6, wherein the (1) data and/or (2) information and/or (3) at least onesignal are further based, at least in part, on the following: at leastone determination of the decay based, at least in part, on whether theat least one device requests access to the one or more content items inresponse to the recommendation.
 9. A method of claim 1, wherein the (1)data and/or (2) information and/or (3) at least one signal are furtherbased, at least in part, on the following: a correlation of the one orlocations to one or more boxes, wherein the one or more boxesrepresented respective one or more bounded geographical areas; andwherein the popularity data is determined with respect to the one ormore boxes.
 10. A method of claim 1, wherein the (1) data and/or (2)information and/or (3) at least one signal are further based, at leastin part, on the following: at least one determination of a maximumpopularity value for the one or more boxes, wherein the popularity datais based, at least in part, on a distribution of the maximum popularityvalue among the one or more content items associated with the respectiveone or more boxes.
 11. An apparatus comprising: at least one processor;and at least one memory including computer program code for one or moreprograms, the at least one memory and the computer program codeconfigured to, with the at least one processor, cause the apparatus toperform at least the following, process and/or facilitate a processingof contextual information associated with at least one device todetermine one or more locations, one or more contextual parametervalues, or a combination thereof; determine popularity data associatedwith one or more content items with respect to the one or morelocations, the one or more contextual parameter values, or acombination, wherein the popularity data is determined from one or moreother devices sharing at least substantially the one or more locations,the one or more contextual parameter values, or a combination thereof;and cause, at least in part, a recommendation of the one or more contentitems to the at least one device based, at least in part, on thepopularity information.
 12. An apparatus of claim 11, wherein theapparatus is further caused to: determine the popularity data based, atleast in part, on a number of requests for the one or more content itemsby the at least one device, the one or other devices, or a combinationthereof when sharing the at least substantially the one or morelocations, the one or more contextual parameter values, or a combinationthereof.
 13. An apparatus of claim 12, wherein the apparatus is furthercaused to: process and/or facilitate a processing of the requests todetermine one or more uniform resource locators (URLs) associated withthe one or more content items; and determine the popularity data based,at least in part, on whether the one or more URLs are one or more toplevel domains.
 14. An apparatus of claim 11, wherein the apparatus isfurther caused to: cause, at least in part, an association of one ormore item locations, one or more item contextual parameters, or acombination thereof with the one or more content items based, at leastin part, on the one or more locations, the one or more contextualparameters, or a combination thereof associated with at least onedevice, the one or more other devices, or a combination thereof whenrequesting the one or more content items.
 15. An apparatus of claim 14,wherein the apparatus is further caused to: determine distanceinformation between the at least one device and the one or more contentinformation based, at least in part, on one or more distances between(a) the one or more item locations and the one or more locations; (b)the one or more item contextual parameters and the one or morecontextual parameters; or (c) a combination thereof, wherein therecommendation of the one or more content items is further based, atleast in part, on the distance information.
 16. An apparatus of claim11, wherein the apparatus is further caused to: cause, at least in part,a decay of the popularity information.
 17. An apparatus of claim 16,wherein the decay is based, at least in part, on one or more temporalparameters, one or more geometric progressions, or a combinationthereof.
 18. An apparatus of claim 16, wherein the apparatus is furthercaused to: determine the decay based, at least in part, on whether theat least one device requests access to the one or more content items inresponse to the recommendation.
 19. An apparatus of claim 11, whereinthe apparatus is further caused to: cause, at least in part, acorrelation of the one or locations to one or more boxes, wherein theone or more boxes represented respective one or more boundedgeographical areas; and wherein the popularity data is determined withrespect to the one or more boxes.
 20. An apparatus of claim 11, whereinthe apparatus is further caused to: determine a maximum popularity valuefor the one or more boxes, wherein the popularity data is based, atleast in part, on a distribution of the maximum popularity value amongthe one or more content items associated with the respective one or moreboxes. 21-48. (canceled)